Visualizing the selective localization of carbon nanotubes in immiscible polymer blends
Bibliographic record
Abstract
Abstract This paper examines the migration mechanisms of multiwalled carbon nanotubes (MWCNTs) in 80/20 polyvinylidenefloride/polystyrene (PVDF/PS) and 80/20 polyvinylidenefloride /polycaprolactone (PVDF/PCL) blends. We contrasted the migration of MWCNTs in a low‐viscosity, semi‐crystalline PCL phase versus that in a high‐viscosity, amorphous PS phase. With a 1 vol% loading of MWCNTs, the PVDF/PCL system required 54% higher processing work relative to the neat blend, while the PVDF/PS system required 360% higher work. A visualization system was used to capture the changes in the structure of the blend throughout the mixing process. The structural changes in the blend were correlated with the processing Work and the morphology through electron microscopy. Using the Young equation, for the PVDF/PCL blend, MWCNT is predicted to have a thermodynamic affinity to migrate to the interface, while preferring to remain in the PVDF phase for the PVDF/PS system. However, in practice, for the PVDF/PCL system, the MWCNTs are localized in the PCL phase, while the MWCNTs remained scattered between the PS and PVDF phases for the PVDF/PS system. The viscoelastic properties of the polymers, specifically the viscosity of the minor component, played a crucial role in the migration mechanism. Due to a better dispersion of MWCNTs, the rheological and electromagnetic properties of the PVDF/PCL system are significantly higher. Highlights In‐situ visualization of carbon nanotubes migration during blending. Examining the influence of viscosity of the blend on the migration of nanofiller. Correlating the torque of mixing to the morphology of blends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".